Miro MCP

Live

OAUTH 2.0

COLLABORATION

Productivity

Connect to Miro MCP to create and manage boards, frames, sticky notes, shapes, diagrams, and comments directly from your AI workflows.

  • Acts as the user: Every tool call runs as the authorizing user. Access and audit trail stay intact.
  • Credentials stay vaulted: AES-256 encrypted, resolved at request time, never stored in LLM context.
  • Scoped before every call: Per-user permissions enforced automatically. 90-day audit trail included.
Miro MCP
agent · Acme Q3
Run
Board Create in Miro MCP
S
miromcp_board_create
85ms
Miro MCP agent
Create a new miro board with a given name and optional description. always confirm with the user before calling this too.
Sources: Miro MCP
miromcpmcp
1 tool call
18:29
Message Claude...

Tools your agent reaches for on Miro boards, scoped per user.

CALL ANY TOOL
Boards, frames, sticky notes, shapes, and comments: full Miro board control, scoped per user.
miromcp_board_create
Create board
Create a new Miro board. To place the board inside a space, pass parent_space_url - either the space URL or the space content item id. Creating it in the space directly saves the extra board_move call that creating it at the team root would need. IMPORTANT: Always confirm with the user before creating a board. This action creates a new board and cannot be undone.
Parameters
Name
Type
Required
Description
name
string
Required
Name of the new board.
description
string
Optional
Optional description for the board.
icon_emoji
string
Optional
Optional single emoji to use as the board icon, e.g. "🚀". Set it when the user asks for an icon; otherwise leave it unset.
invocation_source
string
Optional
Identifies what triggered this tool call.
is_repository
boolean
Optional
Set to true when operating in a source-control repository folder, false otherwise.
parent_space_url
string
Optional
Space to create the content inside, given either as a full Miro space URL (e.g., 'https://miro.com/app/dashboard/space/0VM5RHCprOu2MnOHXLqp3f') or as the space content item id on its own (e.g., '0VM5RHCprOu2MnOHXLqp3f'). Omit to create at the team root instead.
miromcp_board_get_space
Board get space
miromcp_board_list_items
Board list items
miromcp_board_move
Move board
miromcp_board_restore
Restore board
miromcp_board_role_update
Update board role
miromcp_board_search_boards
Board search boards
miromcp_canvas_create_from_svg
Canvas create from svg
miromcp_canvas_load_format_skill
Canvas load format skill
miromcp_canvas_read_as_svg
Canvas read as svg
miromcp_canvas_update_from_svg
Canvas update from svg
miromcp_code_widget_delete
Delete code widget
miromcp_code_widget_get
Get code widget
miromcp_comment_list_comments
Comment list comments
miromcp_comment_resolve
Resolve comment
miromcp_diagram_create
Create diagram
miromcp_diagram_get_dsl
Diagram get dsl
miromcp_diagram_update_mermaid
Diagram update mermaid
miromcp_doc_get
Get doc
miromcp_image_create
Create image
miromcp_image_get_upload_url
Image get upload url
miromcp_image_resource_upload
Upload image resource
miromcp_layout_get_dsl
Layout get dsl
miromcp_layout_update
Update layout
miromcp_preview_resource_poll
Preview resource poll
miromcp_section_create
Create section
miromcp_space_list
List space
miromcp_space_list_children
Space list children
miromcp_space_role_update
Update space role
miromcp_table_sync_rows
Table sync rows

For more tools, view docs.

Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="miromcp")

mcp = MultiServerMCPClient({
"miromcp": {
"url": "https://mcp.scalekit.com/miromcp",
"headers": {"Authorization": "Bearer " + token}
}
})
tools = await mcp.get_tools()
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "miromcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/miromcp
// Pass: Authorization: Bearer + token
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "miromcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/miromcp
// Pass: Authorization: Bearer + token
from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="miromcp")
# Connect to MCP at https://mcp.scalekit.com/miromcp
# Pass: Authorization: Bearer + token
Try these prompts
Copy any prompt into your agent. Each maps directly to a Miro tool. Click to copy, paste into your agent, done.
Get started
Copy the prompt
Copied
Returns the identity of the current authenticated user?
Copy the prompt
Copied
Add or update rows in a Miro table?
Advanced
Copy the prompt
Copied
Get rows from a Miro table with column metadata?
Copy the prompt
Copied
Create a table on a Miro board with specified columns?
SEE HOW AUTH WORKS
Your users connect once. Their Miro credentials stay vaulted, every call is scope-checked, and every action is logged.
1
Authorize
Your user connects
Miro MCP
once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access
Who:
user ‘A’
when:
Once per user
access:
Limited to user
2
Store
Their
Miro MCP
token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection
vault:
encrypted
scope:
per-user
tokens:
auto-refreshed
3
Resolve
When your agent calls a
Miro MCP
tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs
speed:
~40ms
check:
before every call
seen by:
nobody
4
Audit
Every
Miro MCP
tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it
history:
90 days
export:
SIEM-ready
logged:
every call
Test other agents
See the same per-user auth pattern across other connectors.
Engineering Teams
DevOps assistant agent
Polls GitHub for failing checks and stale PRs, opens Linear issues for the ones that need work, and posts a daily digest to Slack. It acts as the engineer, not a shared service account.
Engineering Teams
Engineering standup agent
Pulls commits from GitHub and GitLab, tracks issue movement in Jira, and posts a per-engineer standup brief to Slack. Each engineer's activity is read on their own delegated OAuth.
Engineering Teams
Auto release notes agent
Reads merged GitHub PRs, groups them into structured release notes, publishes the page to Notion, and announces the release in Slack. Every call runs on the engineer's own delegated OAuth.
Engineering Teams
Slack triage
Polls Slack for new messages, classifies bugs and support requests with a LangGraph router, files GitHub issues or Zendesk tickets, and confirms in the thread.
Test other agents
See the same per-user auth pattern across other connectors.
ENGINEERING
Engineering standup agent
Pull commits from GitHub and GitLab, track Jira issue movement, and post a per-engineer standup brief to Slack.
ENGINEERING
DevOps assistant agent
Poll GitHub for failing checks and stale pull requests, open Linear issues for the ones that need work, and digest to Slack.
ENGINEERING
Auto-release notes agent
Group merged GitHub PRs into structured release notes, publish the page to Notion, and announce the release in Slack.
ENGINEERING
Slack triage agent
Classify new Slack messages as bugs or support requests, file the GitHub issue or Zendesk ticket, and reply in the thread.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
01.
Shared tokens break per-user analytics
A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
One connector today. Ten tomorrow.
“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”
Venu Madhav Kattagoni
Head of Engineering / Von
FAQs
Frequently Asked Questions

Does the agent access Miro as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Miro OAuth token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Miro?
Yes. Pass a tool name filter to listScopedTools so the productivity agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Miro.

What happens when a user revokes Miro access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Which boards can the agent create content on?
Only boards the authorizing user can edit. Sticky notes, shapes, frames, and comments attribute to that user in Miro, so board history shows real authorship.

What other MCP connectors does Scalekit support?
Scalekit runs the Typeform MCP server, Rize MCP server, and Privacy MCP server on the same per-user auth, token vault, and audit trail. Browse 500+ connectors in the MCP connector directory.

Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""miromcp"": {
""url"": ""https://mcp.scalekit.com/miromcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.miromcp]
url = ""https://mcp.scalekit.com/miromcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""miromcp"": {
""url"": ""https://mcp.scalekit.com/miromcp"",
""type"": ""http""
}
}
}